1. Field
This disclosure generally relates to image processing and object recognition.
2. Background Art
Recent advances in computer networking and image processing have led to widespread availability of street-level imagery on the World Wide Web (i.e., “the web”). Much of this imagery is systematically gathered through large-scale efforts. The process of gathering images of public spaces, however, often leads to the capture of license plates, faces, and other information considered sensitive from a privacy standpoint.
Some large-scale image gathering efforts employ strategies to intentionally obscure sensitive information appearing in captured images. Such an approach, however, presents a formidable challenge. For one reason, large-scale image gathering efforts produce an enormous amount of imagery that must be processed using fully automatic optimized algorithms running on large computing platforms.
In order to ensure privacy, objects such as faces and license plates must be automatically detected and obscured in the published image. Reliable object detection is difficult, however, due to the fact that there is little control over the conditions of image capture, and thus, the appearance of objects can vary widely. People in captured images may appear close to the camera or in the background, in shadows or partially obscured by other objects. Image detection algorithms may exhibit false positives or alternatively, fail to detect faces that are clearly recognizable to an observer.
The detection of license plates is also challenging. Variations in viewing angle, the presence of shadows or obstructions, as well as variations in the appearance of license plates across geographic areas, pose difficulties even for state-of-the-art object detection algorithms.
The recall percentage of an object detection algorithm describes the algorithm's ability to detect a certain type of object. Algorithms that are tuned for high recall also necessarily produce false positives. From a privacy perspective, however, false positives are preferable to situations in which recognizable faces are not found by the algorithm. An ideal algorithm would exhibit 100% recall with no false positives. This, however, is beyond the reach of state-of-art automatic methods.
Lastly, it is important to preserve the quality of images while achieving high recall. This requires one to control the false positive rate and to obscure faces and license plates in a manner unobtrusive enough so that a viewer's eyes are not drawn to erroneously obscured regions.